Arbeitspapier
Robust maximum detection: Full information best choice problem under multiple priors
We consider a robust version of the full information best choice problem (Gilbert and Mosteller (1966)): there is ambiguity (represented by a set of priors) about the measure driving the observed process. We solve the problem under a very general class of multiple priors in the setting of Riedel (2009). As in the classical case, it is optimal to stop if the current observation is a running maximum that exceeds certain thresholds. We characterize the decreasing sequence of thresholds, as well as the (history dependent) minimizing measure. We introduce locally constant ambiguity neighborhood (LCAn) which has connections to coherent risk measures. Sensitivity analysis is performed using LCAn and exponential neighborhood from Riedel (2009).
- Language
-
Englisch
- Bibliographic citation
-
Series: Center for Mathematical Economics Working Papers ; No. 580
- Classification
-
Wirtschaft
- Event
-
Geistige Schöpfung
- (who)
-
Obradović, Lazar
- Event
-
Veröffentlichung
- (who)
-
Bielefeld University, Center for Mathematical Economics (IMW)
- (where)
-
Bielefeld
- (when)
-
2018
- Handle
- URN
-
urn:nbn:de:0070-pub-29169338
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Obradović, Lazar
- Bielefeld University, Center for Mathematical Economics (IMW)
Time of origin
- 2018
Other Objects (12)
Robust Maximum Detection: Full Information Best Choice Problem under Multiple Priors
Stance Detection Benchmark: How Robust is Your Stance Detection?
A novel, robust quantum detection scheme
Robust Stability of Differential Equations with Maximum
Robust path choice in networks with failures
Multiple choice allocations with small maximum loads
Maximum Likelihood Estimator for Choice-Based Samples
Robust laughter detection for wearable wellbeing sensing
Robust change-point detection and dependence modeling
Robust facial landmark detection in the wild
Robust stimulus detection with imprecise spiking phase
Steady-state fault detection with full-flight data
Robust Maximum Detection: Full Information Best Choice Problem under Multiple Priors
Stance Detection Benchmark: How Robust is Your Stance Detection?
A novel, robust quantum detection scheme
Robust Stability of Differential Equations with Maximum
Robust path choice in networks with failures
Multiple choice allocations with small maximum loads
Maximum Likelihood Estimator for Choice-Based Samples
Robust laughter detection for wearable wellbeing sensing
Robust change-point detection and dependence modeling
Robust facial landmark detection in the wild
Robust stimulus detection with imprecise spiking phase
Steady-state fault detection with full-flight data
Robust Maximum Detection: Full Information Best Choice Problem under Multiple Priors
Stance Detection Benchmark: How Robust is Your Stance Detection?
A novel, robust quantum detection scheme
Robust Stability of Differential Equations with Maximum
Robust path choice in networks with failures
Multiple choice allocations with small maximum loads
Maximum Likelihood Estimator for Choice-Based Samples
Robust laughter detection for wearable wellbeing sensing
Robust change-point detection and dependence modeling
Robust facial landmark detection in the wild
Robust stimulus detection with imprecise spiking phase